An Efficient and Reconfigurable Synchronous Neuron Model

Abstract

This brief presents a reconfigurable and efficient 2-D neuron model capable of extending to higher dimensions. The model is applied to the Izhikevich and FitzHugh-Nagumo neuron models as 2-D case studies and to the Hindmarsh-Rose model as a 3-D case study. Hardware synthesis and physical implementations show that the resulting circuits can reproduce neural dynamics with acceptable precision and considerably low hardware overhead compared to previously published piecewise linear models.